ArticleScientific reports2024
Breast cancer diagnosis using support vector machine optimized by improved quantum inspired grey wolf optimization.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers, 1 of them a synthesis that pooled it.
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Who cites it
30 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Artificial Intelligence Pipeline for Mammography-Based Breast Cancer Detection: An Integrated Systematic Review and Large-Scale Experimental Validation.Medicina (Kaunas, Lithuania) · 2025Pooled it
- A comparative study of quantum-inspired PSO and EA with their binary variants for heart disease classification.Scientific reports · 2026Article
- Enhancing Winters exponential smoothing: a novel approach with back-casting, Grey Wolf Optimization, and error tracking.Scientific reports · 2026Article
- Early retinal disease detection from fundus images using deep neural networks.Scientific reports · 2026Article
- Magnification-independent breast cancer diagnosis using a GWO-enhanced vision transformer with multi-stage stain normalization.Scientific reports · 2026Article
- Breast Cancer Classification Using Feature Selection via Improved Simulated Annealing and SVM Classifier.Diagnostics (Basel, Switzerland) · 2026Article
- AdaptiveInvolutionNet: Spatially-adaptive involution with channel-wise attention for breast MRI tumor classification.PloS one · 2026Article
- Classification of breast cancer using hybridization version of walrus and particle swarm optimization algorithm.Scientific reports · 2025Article
- A novel clustered-based binary grey wolf optimizer to solve the feature selection problem for uncovering the genetic links between non-Hodgkin lymphomas and rheumatologic diseases.Health information science and systems · 2025Article
- A novel hybrid feature selection method combining binary grey wolf optimization and cuckoo search.Scientific reports · 2025Article
- Machine Learning Models for Predicting Gynecological Cancers: Advances, Challenges, and Future Directions.Cancers · 2025Review
- Quantum Oncology: The Applications of Quantum Computing in Cancer Research.Journal of medical systems · 2025Review
- Diagnostic, Therapeutic, and Prognostic Applications of Artificial Intelligence (AI) in the Clinical Management of Brain Metastases (BMs).Brain sciences · 2025Review
- A novel double machine learning approach for detecting early breast cancer using advanced feature selection and dimensionality reduction techniques.Scientific reports · 2025Article
- An explainable AI-driven deep neural network for accurate breast cancer detection from histopathological and ultrasound images.Scientific reports · 2025Article
- Multi-objective quantum hybrid evolutionary algorithms for enhancing quality-of-service in internet of things.Scientific reports · 2025Article
- Advanced leukocyte classification using attention mechanisms and dual channel U-Net architecture.Scientific reports · 2025Article
- SGA-Driven feature selection and random forest classification for enhanced breast cancer diagnosis: A comparative study.Scientific reports · 2025Article
- Development of weighted residual RNN model with hybrid heuristic algorithm for movement recognition framework in ambient assisted living.Scientific reports · 2025Article
- A quantum-optimized approach for breast cancer detection using SqueezeNet-SVM.Scientific reports · 2025Article
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Authors and funding
6 authors.
Funding
Abstract
A prompt diagnosis of breast cancer in its earliest phases is necessary for effective treatment. While Computer-Aided Diagnosis systems play a crucial role in automated mammography image processing, interpretation, grading, and early detection of breast cancer, existing approaches face limitations in achieving optimal accuracy. This study addresses these limitations by hybridizing the improved quantum-inspired binary Grey Wolf Optimizer with the Support Vector Machines Radial Basis Function Kernel. This hybrid approach aims to enhance the accuracy of breast cancer classification by determining the optimal Support Vector Machine parameters. The motivation for this hybridization lies in the need for improved classification performance compared to existing optimizers such as Particle Swarm Optimization and Genetic Algorithm. Evaluate the efficacy of the proposed IQI-BGWO-SVM approach on the MIAS dataset, considering various metric parameters, including accuracy, sensitivity, and specificity. Furthermore, the application of IQI-BGWO-SVM for feature selection will be explored, and the results will be compared. Experimental findings demonstrate that the suggested IQI-BGWO-SVM technique outperforms state-of-the-art classification methods on the MIAS dataset, with a resulting mean accuracy, sensitivity, and specificity of 99.25%, 98.96%, and 100%, respectively, using a tenfold cross-validation datasets partition.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.